Lead Product Manager (Recommendations)

Join Scribd as a Lead Product Manager (Recommendations) and take ownership of the recommendations experience for 200 million monthly visitors. You'll work at the intersection of machine learning and product development, crafting a compelling vision and defining metrics that truly matter. You'll partner with various teams, including ML Engineering and Applied Research, to translate cutting-edge research into production systems. Additionally, you'll define key success metrics, balance short-term wins with long-term vision, and communicate effectively with stakeholders.

Missions

• Définir une vision convaincante pour l'expérience de recommandation de Scribd, en élaborant des métriques significatives et en expérimentant pour atteindre des fonctionnalités révolutionnaires.
• Établir et surveiller les indicateurs de réussite des recommandations, tels que le taux d'engagement, le taux de clics, l'achèvement du contenu et la conversion et la rétention des abonnements.
• Collaborer avec les équipes d'ingénierie ML et de recherche appliquée pour traduire les recherches de pointe en systèmes de production qui intègrent des signaux collaboratifs, des embeddings de contenu et des données comportementales en temps réel.

Profil recherché

- Exceptional written and verbal communication skills - adept at crafting product briefs and presenting data-backed decisions to senior leadership

- Deep familiarity with retrieval and ranking algorithms, embeddings, and feature stores - paired with the ability to reason about end-to-end customer journeys for distinct user segments

- 8+ years of product management experience, including 4+ years leading recommendations or search products in a high-traffic consumer environment

- Demonstrated success shipping ML-driven features that moved core business metrics (engagement, conversion, or revenue) at scale

- Track record of thriving amid ambiguity: shaping a multi-year vision, aligning cross-functional teams, and delivering incremental wins along the way

- Hands-on proficiency with AI tools for productivity and analytics - including LLM-powered workflows, SQL copilots, and data exploration tools - to move fast, prototype ideas, and pressure-test assumptions without always needing engineering support

- Experience designing LLM- and GenAI-enhanced discovery experiences that go beyond raw recommendations to deliver personalized, task-specific value

- Familiarity with modern ML ops tooling